COLOUR ANALYSIS SC22 dataset

## [1] "C:/Users/kadwolf/OneDrive - UGent/UGent-PC/Kadwolf/Documents/00_SPINCITY/05_onderzoek/03_colour_analysis/spin_city_colour_results_V1"

##  [1] "spider"            "predict_red"       "predict_green"    
##  [4] "predict_blue"      "sigma_red"         "sigma_green"      
##  [7] "sigma_blue"        "cor_red"           "cor_green"        
## [10] "cor_blue"          "avg_reflectance"   "sigma_reflectance"

##  [1] "downloadlocatie" "photoID"         "SPIDER_IMAGE"    "SPIDERid"       
##  [5] "mv"              "old_photo_name"  "new_photo_name"  "spider"         
##  [9] "remark"          "spider_length"   "abdomen_length"  "cross_length"   
## [13] "cross_width"     "abdomen_area"

## [1] "spider"         "spider_length"  "abdomen_length" "cross_length"  
## [5] "cross_width"    "abdomen_area"   "remark"

##  [1] "spider"            "predict_red"       "predict_green"    
##  [4] "predict_blue"      "sigma_red"         "sigma_green"      
##  [7] "sigma_blue"        "cor_red"           "cor_green"        
## [10] "cor_blue"          "avg_reflectance"   "sigma_reflectance"
## [13] "spider_length"     "abdomen_length"    "cross_length"     
## [16] "cross_width"       "abdomen_area"      "remark"

## [1] "spider"          "date"            "project_year"    "location"       
## [5] "plotid"          "U_landscape"     "U_local"         "urb_cat"        
## [9] "sampling_period"

##  [1] "spider"            "predict_red"       "predict_green"    
##  [4] "predict_blue"      "sigma_red"         "sigma_green"      
##  [7] "sigma_blue"        "cor_red"           "cor_green"        
## [10] "cor_blue"          "avg_reflectance"   "sigma_reflectance"
## [13] "spider_length"     "abdomen_length"    "cross_length"     
## [16] "cross_width"       "abdomen_area"      "remark"           
## [19] "date"              "project_year"      "location"         
## [22] "plotid"            "U_landscape"       "U_local"          
## [25] "urb_cat"           "sampling_period"

analysis data collected in 2022

SAMPLING INFO CONTAINS: info about two sampling periods (of importance as they bias the sampling sizes of the groups) sampling period 1: +- 8 spiders were sampled (+- 5 spider in web hub for behavioural analysis and 3 spiders in retreat, as for the combined microclimatic study) sampling period 2: +- 10 spiders were sampled to obtain female spiders that would deposit egg sacs for the common garden experiment

no medium local scale locations were measured: so only 2 urbanisation levels at local scale: LOW and HIGH at landscape scale the 3 urbanisation levels were included: LOW MEDIUM HIGH

## spc_tbl_ [625 × 26] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
##  $ spider           : chr [1:625] "SC22COUP01" "SC22COUP02" "SC22COUP03" "SC22COUP04" ...
##  $ predict_red      : num [1:625] 34.7 16.8 20.2 26.6 18.3 ...
##  $ predict_green    : num [1:625] 27.3 15.2 18.5 23.5 17.8 ...
##  $ predict_blue     : num [1:625] 14 10.9 14 15.6 13.6 ...
##  $ sigma_red        : num [1:625] 1.33 1.3 1.17 1.57 1.2 ...
##  $ sigma_green      : num [1:625] 1.286 1.26 0.989 1.284 1.14 ...
##  $ sigma_blue       : num [1:625] 1.62 1.57 1.84 1.47 1.55 ...
##  $ cor_red          : num [1:625] 0.999 0.999 0.999 0.998 0.999 ...
##  $ cor_green        : num [1:625] 0.999 0.999 0.999 0.999 0.999 ...
##  $ cor_blue         : num [1:625] 0.998 0.998 0.998 0.998 0.998 ...
##  $ avg_reflectance  : num [1:625] 25.3 14.3 17.6 21.9 16.6 ...
##  $ sigma_reflectance: num [1:625] 0.82 0.798 0.798 0.836 0.757 ...
##  $ spider_length    : num [1:625] 10.5 12.7 16.7 10.5 13.8 ...
##  $ abdomen_length   : num [1:625] 7.03 10.09 13.62 7.31 11.04 ...
##  $ cross_length     : num [1:625] 2.82 3.57 6.38 3.18 5.9 ...
##  $ cross_width      : num [1:625] 1.68 3.1 4.39 2.13 4.23 ...
##  $ abdomen_area     : num [1:625] 33.9 66.3 120.2 30.7 77.3 ...
##  $ remark           : chr [1:625] NA NA NA NA ...
##  $ date             : chr [1:625] "28/09/2022" "28/09/2022" "28/09/2022" "28/09/2022" ...
##  $ project_year     : chr [1:625] "SC22" "SC22" "SC22" "SC22" ...
##  $ location         : chr [1:625] "P01SR" "P01SR" "P01SR" "P01SR" ...
##  $ plotid           : chr [1:625] "P01" "P01" "P01" "P01" ...
##  $ U_landscape      : chr [1:625] "HIGH" "HIGH" "HIGH" "HIGH" ...
##  $ U_local          : chr [1:625] "HIGH" "HIGH" "HIGH" "HIGH" ...
##  $ urb_cat          : chr [1:625] "HIGHHIGH" "HIGHHIGH" "HIGHHIGH" "HIGHHIGH" ...
##  $ sampling_period  : chr [1:625] "period2" "period2" "period2" "period2" ...
##  - attr(*, "spec")=
##   .. cols(
##   ..   spider = col_character(),
##   ..   predict_red = col_double(),
##   ..   predict_green = col_double(),
##   ..   predict_blue = col_double(),
##   ..   sigma_red = col_double(),
##   ..   sigma_green = col_double(),
##   ..   sigma_blue = col_double(),
##   ..   cor_red = col_double(),
##   ..   cor_green = col_double(),
##   ..   cor_blue = col_double(),
##   ..   avg_reflectance = col_double(),
##   ..   sigma_reflectance = col_double()
##   .. )
##  - attr(*, "problems")=<externalptr>

##  [1] "P01SG" "P01SR" "P02SG" "P02SR" "P03SG" "P03SR" "P04SG" "P04SR" "P05SG"
## [10] "P05SR" "P06SG" "P06SR" "P07SG" "P07SR" "P08SG" "P08SR" "P09SG" "P09SR"
## [19] "P10SG" "P10SR" "P11SG" "P11SR" "P12SG" "P12SR" "P13SG" "P13SR" "P14SG"
## [28] "P14SR" "P15SG" "P15SR" "P16SG" "P16SR" "P17SG" "P17SR" "P18SG" "P18SR"
## [37] "P19SG" "P19SR" "P20SG" "P20SR" "P21SG" "P21SR" "P22SG" "P22SR" "P23SG"
## [46] "P23SR" "P24SG" "P24SR" "P25SG" "P25SR" "P26SG" "P26SR" "P27SG" "P27SR"

## [1] "HIGH"   "LOW"    "MEDIUM"

## [1] "HIGH" "LOW"

## [1] "HIGHHIGH"   "HIGHLOW"    "LOWHIGH"    "LOWLOW"     "MEDIUMHIGH"
## [6] "MEDIUMLOW"

## Warning: package 'chron' was built under R version 4.3.2

## [1] TRUE

## [1] "period1" "period2"

## Rows: 625
## Columns: 27
## $ spider            <fct> SC22COUP01, SC22COUP02, SC22COUP03, SC22COUP04, SC22…
## $ predict_red       <dbl> 34.71614, 16.84413, 20.15878, 26.60387, 18.32356, 20…
## $ predict_green     <dbl> 27.25207, 15.15258, 18.53625, 23.51236, 17.75895, 18…
## $ predict_blue      <dbl> 14.03944, 10.87221, 13.96026, 15.61937, 13.57853, 12…
## $ sigma_red         <dbl> 1.334946, 1.300878, 1.173223, 1.572239, 1.203629, 1.…
## $ sigma_green       <dbl> 1.2856570, 1.2596986, 0.9888183, 1.2836398, 1.139792…
## $ sigma_blue        <dbl> 1.619756, 1.567962, 1.838322, 1.474733, 1.553351, 1.…
## $ cor_red           <dbl> 0.9987247, 0.9987824, 0.9990061, 0.9982303, 0.998952…
## $ cor_green         <dbl> 0.9988228, 0.9988620, 0.9992953, 0.9988252, 0.999063…
## $ cor_blue          <dbl> 0.9982353, 0.9983422, 0.9976904, 0.9984812, 0.998330…
## $ avg_reflectance   <dbl> 25.33589, 14.28964, 17.55176, 21.91187, 16.55368, 17…
## $ sigma_reflectance <dbl> 0.8204745, 0.7984448, 0.7981674, 0.8362949, 0.757242…
## $ spider_length     <dbl> 10.503, 12.735, 16.714, 10.501, 13.850, 12.557, 16.9…
## $ abdomen_length    <dbl> 7.031, 10.094, 13.625, 7.313, 11.044, 10.447, 14.443…
## $ cross_length      <dbl> 2.816, 3.574, 6.385, 3.183, 5.903, 4.280, 6.292, 4.2…
## $ cross_width       <dbl> 1.676, 3.097, 4.387, 2.131, 4.232, 4.440, 5.881, 4.2…
## $ abdomen_area      <dbl> 33.935, 66.292, 120.207, 30.731, 77.255, 72.300, 139…
## $ remark            <chr> NA, NA, NA, NA, NA, NA, "CROSS WIDTH NOT CLEAR", NA,…
## $ date              <date> 2022-09-28, 2022-09-28, 2022-09-28, 2022-09-28, 202…
## $ project_year      <fct> SC22, SC22, SC22, SC22, SC22, SC22, SC22, SC22, SC22…
## $ location          <fct> P01SR, P01SR, P01SR, P01SR, P01SR, P01SR, P01SR, P01…
## $ plotid            <fct> P01, P01, P01, P01, P01, P01, P01, P01, P01, P01, P0…
## $ U_landscape       <fct> HIGH, HIGH, HIGH, HIGH, HIGH, HIGH, HIGH, HIGH, HIGH…
## $ U_local           <fct> HIGH, HIGH, HIGH, HIGH, HIGH, HIGH, HIGH, HIGH, HIGH…
## $ urb_cat           <fct> HIGHHIGH, HIGHHIGH, HIGHHIGH, HIGHHIGH, HIGHHIGH, HI…
## $ sampling_period   <fct> period2, period2, period2, period2, period2, period2…
## $ day               <dbl> 270, 270, 270, 270, 270, 270, 270, 270, 270, 248, 24…

## [1] "cross_length" "cross_width"  "remark"

## # A tibble: 5 × 27
##   spider predict_red predict_green predict_blue sigma_red sigma_green sigma_blue
##   <fct>        <dbl>         <dbl>        <dbl>     <dbl>       <dbl>      <dbl>
## 1 SC22P…        27.3          22.2        12.9      2.13        2.23        2.20
## 2 SC22P…        22.5          19.0        12.9      2.28        2.41        2.33
## 3 SC22P…        23.6          15.5         9.78     1.32        1.21        1.48
## 4 SC22P…        40.1          29.7        16.1      1.18        1.22        1.18
## 5 SC22P…        17.2          16.9        13.2      0.992       0.875       1.60
## # ℹ 20 more variables: cor_red <dbl>, cor_green <dbl>, cor_blue <dbl>,
## #   avg_reflectance <dbl>, sigma_reflectance <dbl>, spider_length <dbl>,
## #   abdomen_length <dbl>, cross_length <dbl>, cross_width <dbl>,
## #   abdomen_area <dbl>, remark <chr>, date <date>, project_year <fct>,
## #   location <fct>, plotid <fct>, U_landscape <fct>, U_local <fct>,
## #   urb_cat <fct>, sampling_period <fct>, day <dbl>

## # A tibble: 5 × 27
##   spider predict_red predict_green predict_blue sigma_red sigma_green sigma_blue
##   <fct>        <dbl>         <dbl>        <dbl>     <dbl>       <dbl>      <dbl>
## 1 SC22P…        27.3          22.2        12.9      2.13        2.23        2.20
## 2 SC22P…        22.5          19.0        12.9      2.28        2.41        2.33
## 3 SC22P…        23.6          15.5         9.78     1.32        1.21        1.48
## 4 SC22P…        40.1          29.7        16.1      1.18        1.22        1.18
## 5 SC22P…        17.2          16.9        13.2      0.992       0.875       1.60
## # ℹ 20 more variables: cor_red <dbl>, cor_green <dbl>, cor_blue <dbl>,
## #   avg_reflectance <dbl>, sigma_reflectance <dbl>, spider_length <dbl>,
## #   abdomen_length <dbl>, cross_length <dbl>, cross_width <dbl>,
## #   abdomen_area <dbl>, remark <chr>, date <date>, project_year <fct>,
## #   location <fct>, plotid <fct>, U_landscape <fct>, U_local <fct>,
## #   urb_cat <fct>, sampling_period <fct>, day <dbl>

in total 625 observations: in more detail

## # A tibble: 6 × 4
## # Groups:   U_landscape, U_local [6]
##   U_landscape U_local period1 period2
##   <fct>       <fct>     <int>   <int>
## 1 LOW         LOW          81      76
## 2 LOW         HIGH         76      NA
## 3 MEDIUM      LOW          76      NA
## 4 MEDIUM      HIGH         76      NA
## 5 HIGH        LOW          79      NA
## 6 HIGH        HIGH         84      77

## # A tibble: 54 × 3
## # Groups:   location [54]
##    location period1 period2
##    <fct>      <int>   <int>
##  1 P01SG          8      NA
##  2 P01SR          8       9
##  3 P02SG          8      NA
##  4 P02SR          9      14
##  5 P03SG         10      NA
##  6 P03SR          8       9
##  7 P04SG          9      NA
##  8 P04SR          9      NA
##  9 P05SG          8      NA
## 10 P05SR          8      NA
## # ℹ 44 more rows

## # A tibble: 2 × 2
##   sampling_period     n
##   <fct>           <int>
## 1 period1           472
## 2 period2           153

#body size measurements first exploration then test each morphological trait via univariate mixed models filter out missing values

DUE TO THE FACT THAT WE HAVE 2 DIFFERENT SAMPLING PERIODS I will analyze them seperately datasets : data_bz_p1 datasets : data_bz_p2

some graphics to show that there is a big difference between the two sampling periods (+ also here already visualy see that spiders collected in 2022 are also bigger in urbanised areas than spiders from lower urbanisation categories) there is a clear difference in the length between both sampling periods and would also significantly bias the sample sizes of certain urbanisation categories (namely the extreme : lowlow and highhigh)

correlation

## Warning: package 'corrplot' was built under R version 4.3.2

for sampling period 2 hardly any relationship of the body size measurements with sampling day

!! but for sampling period 1 a strong correlation

so i split up per sampling period

##sampling period 1

always the same 3 type of visualisations are repeated

length - period 1

abdomen area - period1

cross size - period1

maybe better to scale the cross length to the proportion it takes of the abdomen or of the spider visualised here #### corrected crosslength - period1

## Warning: package 'ggpubr' was built under R version 4.3.2

statistics make use of glmmTMB multiple observation for each location because of strong positive correlation for the body size measurements with sampling day here i do include scaled day in statistical models

statistics

question: I found 3 different options if multiple observations per subplot are present:

random effect choice: masterthesis of Lukas and Thomas : we chose for the unique subplotid which is the same as the location (sampling location): (1|location)

an analysis proposed during speedy and used in masterthesis 2014 carabids Daan Mertens(supervised by Frederik Hendrickx and myself): we use plotid and the urbanisation levels at local/subplot scale for morphological measurements: (1|plotid/U_local)

Analysis spiders: Dahirel_et_al_2018_JAnimEcol/ eg. modA <- MCMCglmm(sSurface ~ landscape_urba + local_urba + sday, random = ~PLOT + PLOT:Site, data = data, family = “gaussian”, verbose = F, pr = TRUE, prior = prior_1b, nitt = niterations, burnin = nburnin, thin = nthin)

uses plotid and location => (1|plotid/location)

(same as in Baardsen et al 2021;“we included SiteID nested in PlotID as random effect in all models”)

• effect of sampling date incorporation

I chose (for now) to use as random factor: (1|plotid/location), as fixed effect: urbanisation level at landscape scale (U_landscape) and urbanisation level at local scale (U_local), their interaction and potential interaction with scaled sampling day (sday): response variable = U_landscape * U_local * sday + (1|plotid/location)

for a certain part of the data, namely sampling period 2, location is the same as plotid, and only difference between 2 urbantisation categorie HIGHHIGH and LOWLOW so here i use as random factor (1|location), as fixed effects urb_cat and scaled sampling day (sday): response variable = urb_cat * sday (1|location)

statistics - spider length - period1

## Warning: package 'DHARMa' was built under R version 4.3.3

## Warning: package 'glmmTMB' was built under R version 4.3.3

## Warning: package 'car' was built under R version 4.3.2

## Warning: package 'carData' was built under R version 4.3.2

## Warning: package 'emmeans' was built under R version 4.3.3

## Warning: package 'performance' was built under R version 4.3.3

## Warning: package 'effects' was built under R version 4.3.2

##  Family: gaussian  ( identity )
## Formula:          
## spider_length ~ U_landscape + U_local + sday + U_landscape:sday +  
##     (1 | plotid/location)
## Data: data_bz_p1
## 
##      AIC      BIC   logLik deviance df.resid 
##   1820.8   1862.3   -900.4   1800.8      457 
## 
## Random effects:
## 
## Conditional model:
##  Groups          Name        Variance Std.Dev.
##  location:plotid (Intercept) 0.09611  0.3100  
##  plotid          (Intercept) 0.16722  0.4089  
##  Residual                    2.58812  1.6088  
## Number of obs: 467, groups:  location:plotid, 54; plotid, 27
## 
## Dispersion estimate for gaussian family (sigma^2): 2.59 
## 
## Conditional model:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             10.7338     0.2201   48.77  < 2e-16 ***
## U_landscapeMEDIUM        0.3020     0.2866    1.05 0.292068    
## U_landscapeHIGH          1.0476     0.2867    3.65 0.000259 ***
## U_localHIGH             -0.3347     0.1722   -1.94 0.051909 .  
## sday                     0.7059     0.2246    3.14 0.001671 ** 
## U_landscapeMEDIUM:sday  -0.3034     0.2938   -1.03 0.301715    
## U_landscapeHIGH:sday     0.5191     0.3018    1.72 0.085352 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: spider_length
##                      Chisq Df Pr(>Chisq)    
## (Intercept)      2378.1307  1  < 2.2e-16 ***
## U_landscape        14.1550  2  0.0008439 ***
## U_local             3.7787  1  0.0519091 .  
## sday                9.8797  1  0.0016711 ** 
## U_landscape:sday    8.9550  2  0.0113618 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

##  contrast      estimate    SE  df t.ratio p.value
##  LOW - MEDIUM    -0.302 0.287 457  -1.054  0.5435
##  LOW - HIGH      -1.048 0.287 457  -3.654  0.0008
##  MEDIUM - HIGH   -0.746 0.286 457  -2.603  0.0258
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 3 estimates

##  contrast   estimate    SE  df t.ratio p.value
##  LOW - HIGH    0.335 0.172 457   1.944  0.0525
## 
## Results are averaged over the levels of: U_landscape

##  contrast  estimate SE df z.ratio p.value
##  (nothing)   nonEst NA NA      NA      NA
## 
## Results are averaged over the levels of: U_landscape, U_local

##  contrast                                                              estimate
##  (LOW sday-5.65912905476366e-16) - (MEDIUM sday-5.65912905476366e-16)    -0.302
##  (LOW sday-5.65912905476366e-16) - (HIGH sday-5.65912905476366e-16)      -1.048
##  (MEDIUM sday-5.65912905476366e-16) - (HIGH sday-5.65912905476366e-16)   -0.746
##     SE  df t.ratio p.value
##  0.287 457  -1.054  0.5435
##  0.287 457  -3.654  0.0008
##  0.286 457  -2.603  0.0258
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 3 estimates

## Warning: package 'ggeffects' was built under R version 4.3.3

not sure if this is correct or appropriate:

##  contrast                        estimate    SE  df t.ratio p.value
##  (LOW sday-1) - (MEDIUM sday-1)  -0.60540 0.412 457  -1.471  0.8684
##  (LOW sday-1) - (HIGH sday-1)    -0.52843 0.400 457  -1.319  0.9253
##  (LOW sday-1) - LOW sday0        -0.70595 0.225 457  -3.143  0.0463
##  (LOW sday-1) - MEDIUM sday0     -1.00795 0.361 457  -2.790  0.1213
##  (LOW sday-1) - HIGH sday0       -1.75352 0.361 457  -4.853  0.0001
##  (LOW sday-1) - LOW sday1        -1.41189 0.449 457  -3.143  0.0463
##  (LOW sday-1) - MEDIUM sday1     -1.41051 0.404 457  -3.488  0.0155
##  (LOW sday-1) - HIGH sday1       -2.97861 0.426 457  -6.988  <.0001
##  (MEDIUM sday-1) - (HIGH sday-1)  0.07697 0.388 457   0.198  1.0000
##  (MEDIUM sday-1) - LOW sday0     -0.10055 0.348 457  -0.289  1.0000
##  (MEDIUM sday-1) - MEDIUM sday0  -0.40256 0.189 457  -2.126  0.4570
##  (MEDIUM sday-1) - HIGH sday0    -1.14812 0.348 457  -3.304  0.0283
##  (MEDIUM sday-1) - LOW sday1     -0.80649 0.416 457  -1.936  0.5887
##  (MEDIUM sday-1) - MEDIUM sday1  -0.80512 0.379 457  -2.126  0.4570
##  (MEDIUM sday-1) - HIGH sday1    -2.37322 0.415 457  -5.724  <.0001
##  (HIGH sday-1) - LOW sday0       -0.17752 0.335 457  -0.531  0.9998
##  (HIGH sday-1) - MEDIUM sday0    -0.47953 0.335 457  -1.433  0.8845
##  (HIGH sday-1) - HIGH sday0      -1.22509 0.201 457  -6.087  <.0001
##  (HIGH sday-1) - LOW sday1       -0.88346 0.405 457  -2.179  0.4213
##  (HIGH sday-1) - MEDIUM sday1    -0.88209 0.381 457  -2.317  0.3337
##  (HIGH sday-1) - HIGH sday1      -2.45019 0.403 457  -6.087  <.0001
##  LOW sday0 - MEDIUM sday0        -0.30201 0.287 457  -1.054  0.9802
##  LOW sday0 - HIGH sday0          -1.04758 0.287 457  -3.654  0.0087
##  LOW sday0 - LOW sday1           -0.70595 0.225 457  -3.143  0.0463
##  LOW sday0 - MEDIUM sday1        -0.70457 0.339 457  -2.077  0.4906
##  LOW sday0 - HIGH sday1          -2.27267 0.365 457  -6.221  <.0001
##  MEDIUM sday0 - HIGH sday0       -0.74557 0.286 457  -2.603  0.1880
##  MEDIUM sday0 - LOW sday1        -0.40394 0.367 457  -1.101  0.9740
##  MEDIUM sday0 - MEDIUM sday1     -0.40256 0.189 457  -2.126  0.4570
##  MEDIUM sday0 - HIGH sday1       -1.97066 0.365 457  -5.401  <.0001
##  HIGH sday0 - LOW sday1           0.34163 0.367 457   0.931  0.9911
##  HIGH sday0 - MEDIUM sday1        0.34301 0.339 457   1.012  0.9847
##  HIGH sday0 - HIGH sday1         -1.22509 0.201 457  -6.087  <.0001
##  LOW sday1 - MEDIUM sday1         0.00138 0.409 457   0.003  1.0000
##  LOW sday1 - HIGH sday1          -1.56672 0.431 457  -3.631  0.0095
##  MEDIUM sday1 - HIGH sday1       -1.56810 0.408 457  -3.848  0.0043
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 9 estimates

I think this is also a good visualisation for spider length

I interprete this as : result: spiders become larger with sampling day. However this increase is much larger (so slope of curve different) between high landscapes and low/medium landscapes. Furthermore, there is also a marginal difference between locally high urbanised and locally low urbanised locations: spider in low urbanised subplots are bigger than spiders in highly urbanised subplots

Extra visualisation tests

statistics - abdomen length - period1

##  Family: gaussian  ( identity )
## Formula:          
## abdomen_length ~ U_landscape + U_local + sday + U_landscape:sday +  
##     (1 | plotid/location)
## Data: data_bz_p1
## 
##      AIC      BIC   logLik deviance df.resid 
##   1768.7   1810.2   -874.3   1748.7      457 
## 
## Random effects:
## 
## Conditional model:
##  Groups          Name        Variance Std.Dev.
##  location:plotid (Intercept) 0.09896  0.3146  
##  plotid          (Intercept) 0.10491  0.3239  
##  Residual                    2.32709  1.5255  
## Number of obs: 467, groups:  location:plotid, 54; plotid, 27
## 
## Dispersion estimate for gaussian family (sigma^2): 2.33 
## 
## Conditional model:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)              8.0093     0.1985   40.34  < 2e-16 ***
## U_landscapeMEDIUM        0.2405     0.2552    0.94 0.345998    
## U_landscapeHIGH          0.7586     0.2550    2.98 0.002928 ** 
## U_localHIGH             -0.5041     0.1660   -3.04 0.002394 ** 
## sday                     0.7474     0.1998    3.74 0.000183 ***
## U_landscapeMEDIUM:sday  -0.1569     0.2615   -0.60 0.548502    
## U_landscapeHIGH:sday     0.5246     0.2682    1.96 0.050482 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: abdomen_length
##                      Chisq Df Pr(>Chisq)    
## (Intercept)      1627.5392  1  < 2.2e-16 ***
## U_landscape         9.2547  2  0.0097804 ** 
## U_local             9.2201  1  0.0023937 ** 
## sday               13.9949  1  0.0001833 ***
## U_landscape:sday    8.1759  2  0.0167733 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

##  contrast      estimate    SE  df t.ratio p.value
##  LOW - MEDIUM    -0.241 0.255 457  -0.942  0.6137
##  LOW - HIGH      -0.759 0.255 457  -2.975  0.0086
##  MEDIUM - HIGH   -0.518 0.255 457  -2.033  0.1054
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 3 estimates

##  contrast   estimate    SE  df t.ratio p.value
##  LOW - HIGH    0.504 0.166 457   3.036  0.0025
## 
## Results are averaged over the levels of: U_landscape

##  contrast  estimate SE df z.ratio p.value
##  (nothing)   nonEst NA NA      NA      NA
## 
## Results are averaged over the levels of: U_landscape, U_local

##  contrast                                                              estimate
##  (LOW sday-5.65912905476366e-16) - (MEDIUM sday-5.65912905476366e-16)    -0.241
##  (LOW sday-5.65912905476366e-16) - (HIGH sday-5.65912905476366e-16)      -0.759
##  (MEDIUM sday-5.65912905476366e-16) - (HIGH sday-5.65912905476366e-16)   -0.518
##     SE  df t.ratio p.value
##  0.255 457  -0.942  0.6137
##  0.255 457  -2.975  0.0086
##  0.255 457  -2.033  0.1054
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 3 estimates

not sure if this is correct or appropriate:

##  contrast                        estimate    SE  df t.ratio p.value
##  (LOW sday-1) - (MEDIUM sday-1)   -0.3974 0.367 457  -1.083  0.9765
##  (LOW sday-1) - (HIGH sday-1)     -0.2340 0.357 457  -0.655  0.9992
##  (LOW sday-1) - LOW sday0         -0.7474 0.200 457  -3.741  0.0064
##  (LOW sday-1) - MEDIUM sday0      -0.9880 0.322 457  -3.066  0.0580
##  (LOW sday-1) - HIGH sday0        -1.5060 0.322 457  -4.677  0.0001
##  (LOW sday-1) - LOW sday1         -1.4949 0.400 457  -3.741  0.0064
##  (LOW sday-1) - MEDIUM sday1      -1.5785 0.361 457  -4.379  0.0005
##  (LOW sday-1) - HIGH sday1        -2.7780 0.379 457  -7.329  <.0001
##  (MEDIUM sday-1) - (HIGH sday-1)   0.1634 0.346 457   0.473  0.9999
##  (MEDIUM sday-1) - LOW sday0      -0.3500 0.310 457  -1.130  0.9694
##  (MEDIUM sday-1) - MEDIUM sday0   -0.5906 0.169 457  -3.502  0.0148
##  (MEDIUM sday-1) - HIGH sday0     -1.1086 0.309 457  -3.584  0.0111
##  (MEDIUM sday-1) - LOW sday1      -1.0975 0.370 457  -2.964  0.0769
##  (MEDIUM sday-1) - MEDIUM sday1   -1.1811 0.337 457  -3.502  0.0148
##  (MEDIUM sday-1) - HIGH sday1     -2.3806 0.368 457  -6.464  <.0001
##  (HIGH sday-1) - LOW sday0        -0.5134 0.298 457  -1.723  0.7322
##  (HIGH sday-1) - MEDIUM sday0     -0.7540 0.298 457  -2.530  0.2202
##  (HIGH sday-1) - HIGH sday0       -1.2720 0.179 457  -7.118  <.0001
##  (HIGH sday-1) - LOW sday1        -1.2609 0.360 457  -3.498  0.0150
##  (HIGH sday-1) - MEDIUM sday1     -1.3445 0.339 457  -3.966  0.0027
##  (HIGH sday-1) - HIGH sday1       -2.5440 0.357 457  -7.118  <.0001
##  LOW sday0 - MEDIUM sday0         -0.2405 0.255 457  -0.942  0.9904
##  LOW sday0 - HIGH sday0           -0.7586 0.255 457  -2.975  0.0746
##  LOW sday0 - LOW sday1            -0.7474 0.200 457  -3.741  0.0064
##  LOW sday0 - MEDIUM sday1         -0.8311 0.302 457  -2.751  0.1332
##  LOW sday0 - HIGH sday1           -2.0306 0.324 457  -6.264  <.0001
##  MEDIUM sday0 - HIGH sday0        -0.5180 0.255 457  -2.033  0.5208
##  MEDIUM sday0 - LOW sday1         -0.5069 0.326 457  -1.555  0.8284
##  MEDIUM sday0 - MEDIUM sday1      -0.5906 0.169 457  -3.502  0.0148
##  MEDIUM sday0 - HIGH sday1        -1.7901 0.324 457  -5.527  <.0001
##  HIGH sday0 - LOW sday1            0.0111 0.326 457   0.034  1.0000
##  HIGH sday0 - MEDIUM sday1        -0.0725 0.302 457  -0.240  1.0000
##  HIGH sday0 - HIGH sday1          -1.2720 0.179 457  -7.118  <.0001
##  LOW sday1 - MEDIUM sday1         -0.0837 0.364 457  -0.230  1.0000
##  LOW sday1 - HIGH sday1           -1.2831 0.383 457  -3.354  0.0241
##  MEDIUM sday1 - HIGH sday1        -1.1995 0.362 457  -3.313  0.0275
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 9 estimates

visualisation of statistics abdomen length

result: results for abdomen length are the same as for spider length

statistics - abdomen area

abdomen area as a potential measure of fecundity

##  Family: gaussian  ( identity )
## Formula:          
## abdomen_area ~ U_landscape + U_local + sday + U_landscape:sday +  
##     (1 | plotid/location)
## Data: data_bz_p1
## 
##      AIC      BIC   logLik deviance df.resid 
##   4049.1   4090.6  -2014.6   4029.1      457 
## 
## Random effects:
## 
## Conditional model:
##  Groups          Name        Variance Std.Dev.
##  location:plotid (Intercept)  12.1     3.478  
##  plotid          (Intercept)  19.6     4.428  
##  Residual                    305.3    17.472  
## Number of obs: 467, groups:  location:plotid, 54; plotid, 27
## 
## Dispersion estimate for gaussian family (sigma^2):  305 
## 
## Conditional model:
##                        Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             42.0922     2.3983  17.551  < 2e-16 ***
## U_landscapeMEDIUM        2.4313     3.1221   0.779 0.436140    
## U_landscapeHIGH          8.7426     3.1214   2.801 0.005097 ** 
## U_localHIGH             -5.1951     1.8840  -2.758 0.005823 ** 
## sday                     8.3182     2.4465   3.400 0.000674 ***
## U_landscapeMEDIUM:sday  -0.8135     3.2000  -0.254 0.799318    
## U_landscapeHIGH:sday     6.6924     3.2865   2.036 0.041719 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: abdomen_area
##                     Chisq Df Pr(>Chisq)    
## (Intercept)      308.0437  1  < 2.2e-16 ***
## U_landscape        8.3639  2  0.0152687 *  
## U_local            7.6042  1  0.0058232 ** 
## sday              11.5600  1  0.0006738 ***
## U_landscape:sday   7.1053  2  0.0286493 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

not sure if this is correct:

##  contrast                        estimate   SE  df t.ratio p.value
##  (LOW sday-1) - (MEDIUM sday-1)    -3.245 4.48 457  -0.724  0.9984
##  (LOW sday-1) - (HIGH sday-1)      -2.050 4.36 457  -0.470  0.9999
##  (LOW sday-1) - LOW sday0          -8.318 2.45 457  -3.400  0.0208
##  (LOW sday-1) - MEDIUM sday0      -10.749 3.94 457  -2.731  0.1398
##  (LOW sday-1) - HIGH sday0        -17.061 3.93 457  -4.336  0.0006
##  (LOW sday-1) - LOW sday1         -16.636 4.89 457  -3.400  0.0208
##  (LOW sday-1) - MEDIUM sday1      -18.254 4.40 457  -4.145  0.0013
##  (LOW sday-1) - HIGH sday1        -32.071 4.64 457  -6.910  <.0001
##  (MEDIUM sday-1) - (HIGH sday-1)    1.195 4.23 457   0.283  1.0000
##  (MEDIUM sday-1) - LOW sday0       -5.073 3.79 457  -1.339  0.9190
##  (MEDIUM sday-1) - MEDIUM sday0    -7.505 2.06 457  -3.638  0.0092
##  (MEDIUM sday-1) - HIGH sday0     -13.816 3.79 457  -3.650  0.0089
##  (MEDIUM sday-1) - LOW sday1      -13.392 4.54 457  -2.952  0.0796
##  (MEDIUM sday-1) - MEDIUM sday1   -15.009 4.13 457  -3.638  0.0092
##  (MEDIUM sday-1) - HIGH sday1     -28.827 4.52 457  -6.384  <.0001
##  (HIGH sday-1) - LOW sday0         -6.268 3.64 457  -1.720  0.7341
##  (HIGH sday-1) - MEDIUM sday0      -8.699 3.64 457  -2.387  0.2935
##  (HIGH sday-1) - HIGH sday0       -15.011 2.19 457  -6.848  <.0001
##  (HIGH sday-1) - LOW sday1        -14.586 4.42 457  -3.303  0.0284
##  (HIGH sday-1) - MEDIUM sday1     -16.204 4.15 457  -3.908  0.0034
##  (HIGH sday-1) - HIGH sday1       -30.021 4.38 457  -6.848  <.0001
##  LOW sday0 - MEDIUM sday0          -2.431 3.12 457  -0.779  0.9974
##  LOW sday0 - HIGH sday0            -8.743 3.12 457  -2.801  0.1179
##  LOW sday0 - LOW sday1             -8.318 2.45 457  -3.400  0.0208
##  LOW sday0 - MEDIUM sday1          -9.936 3.70 457  -2.689  0.1546
##  LOW sday0 - HIGH sday1           -23.753 3.98 457  -5.973  <.0001
##  MEDIUM sday0 - HIGH sday0         -6.311 3.12 457  -2.023  0.5279
##  MEDIUM sday0 - LOW sday1          -5.887 4.00 457  -1.473  0.8677
##  MEDIUM sday0 - MEDIUM sday1       -7.505 2.06 457  -3.638  0.0092
##  MEDIUM sday0 - HIGH sday1        -21.322 3.97 457  -5.365  <.0001
##  HIGH sday0 - LOW sday1             0.424 4.00 457   0.106  1.0000
##  HIGH sday0 - MEDIUM sday1         -1.193 3.69 457  -0.323  1.0000
##  HIGH sday0 - HIGH sday1          -15.011 2.19 457  -6.848  <.0001
##  LOW sday1 - MEDIUM sday1          -1.618 4.46 457  -0.363  1.0000
##  LOW sday1 - HIGH sday1           -15.435 4.70 457  -3.286  0.0299
##  MEDIUM sday1 - HIGH sday1        -13.817 4.44 457  -3.113  0.0506
## 
## Results are averaged over the levels of: U_local 
## P value adjustment: tukey method for comparing a family of 9 estimates

result : same interpretation as for spider length and abdomen length

sampling period 2

## # A tibble: 15 × 3
## # Groups:   location [15]
##    location urb_cat      n
##    <fct>    <fct>    <int>
##  1 P01SR    HIGHHIGH     9
##  2 P02SR    HIGHHIGH    14
##  3 P03SR    HIGHHIGH     9
##  4 P08SG    LOWLOW      11
##  5 P09SG    LOWLOW      13
##  6 P11SR    HIGHHIGH    11
##  7 P12SR    HIGHHIGH    13
##  8 P16SG    LOWLOW       1
##  9 P17SG    LOWLOW       7
## 10 P18SG    LOWLOW      11
## 11 P19SR    HIGHHIGH    11
## 12 P21SR    HIGHHIGH    10
## 13 P25SG    LOWLOW      11
## 14 P26SG    LOWLOW       7
## 15 P27SG    LOWLOW      15

## # A tibble: 2 × 2
##   urb_cat      n
##   <fct>    <int>
## 1 LOWLOW      76
## 2 HIGHHIGH    77

only 1 spider sampled at P16SG in the second sample period : I remove this location as we have seen before that quite some variation exist between individuals of the same location)

####length sampled only High-high and low-low urbanisation categories

seems that spiders are bigger in more urbanised locations however a lot of variation between the different locations although urbanisation category is the same

abdomen area

result for abdomen area it the same as for spider length and abdomen length roughly the same results but a lot of variation between locations

cross size - period2

indicates that cross pattern is roughly the same

corrected crosslength - period2

statistics - spider length - period2

because no correlation with sampling day, i left this out of the analysis we want the difference between the urbanisation categories highhigh and lowlow

multiple spider within 1 sampling location (location is here the same as plotid, only 1 sampling location per plotid) **so here i use (1|location) and urb_cat (highhigh vs lowlow) response variable = urb_cat + (1|location)

##  Family: gaussian  ( identity )
## Formula:          spider_length ~ urb_cat + (1 | location)
## Data: data_bz_p2
## 
##      AIC      BIC   logLik deviance df.resid 
##    625.0    637.1   -308.5    617.0      148 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance Std.Dev.
##  location (Intercept) 0.4777   0.6911  
##  Residual             3.1003   1.7608  
## Number of obs: 152, groups:  location, 14
## 
## Dispersion estimate for gaussian family (sigma^2):  3.1 
## 
## Conditional model:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      12.0272     0.3337   36.04   <2e-16 ***
## urb_catHIGHHIGH   0.7187     0.4696    1.53    0.126    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: spider_length
##                 Chisq Df Pr(>Chisq)    
## (Intercept) 1298.9939  1     <2e-16 ***
## urb_cat        2.3421  1     0.1259    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##  Family: gaussian  ( identity )
## Formula:          spider_length ~ urb_cat
## Data: data_bz_p2
## 
##      AIC      BIC   logLik deviance df.resid 
##    630.8    639.9   -312.4    624.8      149 
## 
## 
## Dispersion estimate for gaussian family (sigma^2): 3.57 
## 
## Conditional model:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      12.0163     0.2182   55.07   <2e-16 ***
## urb_catHIGHHIGH   0.7171     0.3066    2.34   0.0193 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: spider_length
##                 Chisq Df Pr(>Chisq)    
## (Intercept) 3032.4184  1    < 2e-16 ***
## urb_cat        5.4708  1    0.01934 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning: package 'MuMIn' was built under R version 4.3.3

## Model selection table 
##              cnd((Int)) dsp((Int)) cnd(urb_cat) random df   logLik  AICc delta
## modA              12.03          +            +   c(l)  4 -308.492 625.3  0.00
## modA_reduced      12.02          +            +         3 -312.419 631.0  5.75
##              weight
## modA          0.946
## modA_reduced  0.054
## Models ranked by AICc(x) 
## Random terms: 
##  c(l): cond(1 | location)

model_name	df	logLik	AICc	delta	weight
modA	4	-308.5	625.3	0.00	0.95
modA_reduced	3	-312.4	631.0	5.75	0.05

## Data: data_bz_p2
## Models:
## modA_reduced: spider_length ~ urb_cat, zi=~0, disp=~1
## modA: spider_length ~ urb_cat + (1 | location), zi=~0, disp=~1
##              Df    AIC    BIC  logLik deviance  Chisq Chi Df Pr(>Chisq)   
## modA_reduced  3 630.84 639.91 -312.42   624.84                            
## modA          4 624.98 637.08 -308.49   616.98 7.8551      1   0.005068 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

result: the model comparison shows that : modA with random effect (1|location) describes better the variation. a lot of variation between sampling locations

but no difference between the urbanisation categories for spider length:

## # A tibble: 1 × 6
##   contrast          estimate    SE    df t.ratio p.value
##   <chr>                <dbl> <dbl> <dbl>   <dbl>   <dbl>
## 1 LOWLOW - HIGHHIGH   -0.719 0.470   148   -1.53   0.128

question: is this following analysis a correct way to identify which locations are different from each other because in the previous test you see that random factor location explains a lot of the variation in spider length

## 
## Call:
## lm(formula = spider_length ~ location, data = data_bz_p2)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -3.923 -1.049 -0.118  1.160  4.960 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   13.877778   0.586847  23.648  < 2e-16 ***
## locationP02SR -1.135063   0.752185  -1.509 0.133579    
## locationP03SR -2.133667   0.829927  -2.571 0.011202 *  
## locationP08SG  0.004768   0.791304   0.006 0.995201    
## locationP09SG -2.365470   0.763421  -3.099 0.002357 ** 
## locationP11SR -0.044232   0.791304  -0.056 0.955504    
## locationP12SR -1.611855   0.763421  -2.111 0.036544 *  
## locationP17SG -2.889778   0.887229  -3.257 0.001417 ** 
## locationP18SG -1.057051   0.791304  -1.336 0.183802    
## locationP19SR -2.085051   0.791304  -2.635 0.009377 ** 
## locationP21SR -0.864778   0.808912  -1.069 0.286908    
## locationP25SG -2.061414   0.791304  -2.605 0.010192 *  
## locationP26SG -1.926349   0.887229  -2.171 0.031626 *  
## locationP27SG -2.726444   0.742309  -3.673 0.000342 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.761 on 138 degrees of freedom
## Multiple R-squared:  0.2394, Adjusted R-squared:  0.1677 
## F-statistic: 3.341 on 13 and 138 DF,  p-value: 0.0001843

## Anova Table (Type III tests)
## 
## Response: spider_length
##              Sum Sq  Df  F value    Pr(>F)    
## (Intercept) 1733.33   1 559.2301 < 2.2e-16 ***
## location     134.63  13   3.3412 0.0001843 ***
## Residuals    427.73 138                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##  contrast      estimate    SE  df t.ratio p.value
##  P01SR - P02SR  1.13506 0.752 138   1.509  0.9639
##  P01SR - P03SR  2.13367 0.830 138   2.571  0.3689
##  P01SR - P08SG -0.00477 0.791 138  -0.006  1.0000
##  P01SR - P09SG  2.36547 0.763 138   3.099  0.1185
##  P01SR - P11SR  0.04423 0.791 138   0.056  1.0000
##  P01SR - P12SR  1.61185 0.763 138   2.111  0.6923
##  P01SR - P17SG  2.88978 0.887 138   3.257  0.0781
##  P01SR - P18SG  1.05705 0.791 138   1.336  0.9870
##  P01SR - P19SR  2.08505 0.791 138   2.635  0.3287
##  P01SR - P21SR  0.86478 0.809 138   1.069  0.9985
##  P01SR - P25SG  2.06141 0.791 138   2.605  0.3471
##  P01SR - P26SG  1.92635 0.887 138   2.171  0.6506
##  P01SR - P27SG  2.72644 0.742 138   3.673  0.0226
##  P02SR - P03SR  0.99860 0.752 138   1.328  0.9877
##  P02SR - P08SG -1.13983 0.709 138  -1.607  0.9415
##  P02SR - P09SG  1.23041 0.678 138   1.814  0.8659
##  P02SR - P11SR -1.09083 0.709 138  -1.538  0.9581
##  P02SR - P12SR  0.47679 0.678 138   0.703  1.0000
##  P02SR - P17SG  1.75471 0.815 138   2.153  0.6633
##  P02SR - P18SG -0.07801 0.709 138  -0.110  1.0000
##  P02SR - P19SR  0.94999 0.709 138   1.339  0.9867
##  P02SR - P21SR -0.27029 0.729 138  -0.371  1.0000
##  P02SR - P25SG  0.92635 0.709 138   1.306  0.9894
##  P02SR - P26SG  0.79129 0.815 138   0.971  0.9994
##  P02SR - P27SG  1.59138 0.654 138   2.432  0.4627
##  P03SR - P08SG -2.13843 0.791 138  -2.702  0.2890
##  P03SR - P09SG  0.23180 0.763 138   0.304  1.0000
##  P03SR - P11SR -2.08943 0.791 138  -2.640  0.3253
##  P03SR - P12SR -0.52181 0.763 138  -0.684  1.0000
##  P03SR - P17SG  0.75611 0.887 138   0.852  0.9999
##  P03SR - P18SG -1.07662 0.791 138  -1.361  0.9847
##  P03SR - P19SR -0.04862 0.791 138  -0.061  1.0000
##  P03SR - P21SR -1.26889 0.809 138  -1.569  0.9512
##  P03SR - P25SG -0.07225 0.791 138  -0.091  1.0000
##  P03SR - P26SG -0.20732 0.887 138  -0.234  1.0000
##  P03SR - P27SG  0.59278 0.742 138   0.799  0.9999
##  P08SG - P09SG  2.37024 0.721 138   3.286  0.0720
##  P08SG - P11SR  0.04900 0.751 138   0.065  1.0000
##  P08SG - P12SR  1.61662 0.721 138   2.241  0.6003
##  P08SG - P17SG  2.89455 0.851 138   3.401  0.0521
##  P08SG - P18SG  1.06182 0.751 138   1.414  0.9787
##  P08SG - P19SR  2.08982 0.751 138   2.784  0.2452
##  P08SG - P21SR  0.86955 0.769 138   1.130  0.9973
##  P08SG - P25SG  2.06618 0.751 138   2.752  0.2616
##  P08SG - P26SG  1.93112 0.851 138   2.269  0.5806
##  P08SG - P27SG  2.73121 0.699 138   3.908  0.0103
##  P09SG - P11SR -2.32124 0.721 138  -3.218  0.0867
##  P09SG - P12SR -0.75362 0.691 138  -1.091  0.9981
##  P09SG - P17SG  0.52431 0.825 138   0.635  1.0000
##  P09SG - P18SG -1.30842 0.721 138  -1.814  0.8660
##  P09SG - P19SR -0.28042 0.721 138  -0.389  1.0000
##  P09SG - P21SR -1.50069 0.741 138  -2.027  0.7483
##  P09SG - P25SG -0.30406 0.721 138  -0.422  1.0000
##  P09SG - P26SG -0.43912 0.825 138  -0.532  1.0000
##  P09SG - P27SG  0.36097 0.667 138   0.541  1.0000
##  P11SR - P12SR  1.56762 0.721 138   2.173  0.6490
##  P11SR - P17SG  2.84555 0.851 138   3.343  0.0615
##  P11SR - P18SG  1.01282 0.751 138   1.349  0.9858
##  P11SR - P19SR  2.04082 0.751 138   2.719  0.2800
##  P11SR - P21SR  0.82055 0.769 138   1.067  0.9985
##  P11SR - P25SG  2.01718 0.751 138   2.687  0.2978
##  P11SR - P26SG  1.88212 0.851 138   2.211  0.6222
##  P11SR - P27SG  2.68221 0.699 138   3.838  0.0131
##  P12SR - P17SG  1.27792 0.825 138   1.548  0.9558
##  P12SR - P18SG -0.55480 0.721 138  -0.769  1.0000
##  P12SR - P19SR  0.47320 0.721 138   0.656  1.0000
##  P12SR - P21SR -0.74708 0.741 138  -1.009  0.9992
##  P12SR - P25SG  0.44956 0.721 138   0.623  1.0000
##  P12SR - P26SG  0.31449 0.825 138   0.381  1.0000
##  P12SR - P27SG  1.11459 0.667 138   1.671  0.9225
##  P17SG - P18SG -1.83273 0.851 138  -2.153  0.6634
##  P17SG - P19SR -0.80473 0.851 138  -0.945  0.9996
##  P17SG - P21SR -2.02500 0.868 138  -2.334  0.5332
##  P17SG - P25SG -0.82836 0.851 138  -0.973  0.9994
##  P17SG - P26SG -0.96343 0.941 138  -1.024  0.9990
##  P17SG - P27SG -0.16333 0.806 138  -0.203  1.0000
##  P18SG - P19SR  1.02800 0.751 138   1.369  0.9839
##  P18SG - P21SR -0.19227 0.769 138  -0.250  1.0000
##  P18SG - P25SG  1.00436 0.751 138   1.338  0.9868
##  P18SG - P26SG  0.86930 0.851 138   1.021  0.9990
##  P18SG - P27SG  1.66939 0.699 138   2.389  0.4937
##  P19SR - P21SR -1.22027 0.769 138  -1.586  0.9469
##  P19SR - P25SG -0.02364 0.751 138  -0.031  1.0000
##  P19SR - P26SG -0.15870 0.851 138  -0.186  1.0000
##  P19SR - P27SG  0.64139 0.699 138   0.918  0.9997
##  P21SR - P25SG  1.19664 0.769 138   1.556  0.9542
##  P21SR - P26SG  1.06157 0.868 138   1.224  0.9942
##  P21SR - P27SG  1.86167 0.719 138   2.590  0.3565
##  P25SG - P26SG -0.13506 0.851 138  -0.159  1.0000
##  P25SG - P27SG  0.66503 0.699 138   0.952  0.9995
##  P26SG - P27SG  0.80010 0.806 138   0.993  0.9993
## 
## P value adjustment: tukey method for comparing a family of 14 estimates

contrast	estimate	SE	df	t.ratio	p.value
P01SR - P17SG	2.889778	0.8872288	138	3.257083	0.0780696
P01SR - P27SG	2.726444	0.7423088	138	3.672925	0.0226102
P08SG - P09SG	2.370238	0.7212461	138	3.286309	0.0720350
P08SG - P17SG	2.894545	0.8512101	138	3.400506	0.0520815
P08SG - P27SG	2.731212	0.6988607	138	3.908092	0.0103288
P09SG - P11SR	-2.321238	0.7212461	138	-3.218371	0.0867053
P11SR - P17SG	2.845546	0.8512101	138	3.342941	0.0614529
P11SR - P27SG	2.682212	0.6988607	138	3.837978	0.0131220

## 
## Call:
## lm(formula = spider_length ~ 1, data = data_bz_p2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.5096 -1.4161 -0.1311  1.2337  6.4144 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  12.3796     0.1565   79.09   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.93 on 151 degrees of freedom

## Model selection table 
##                (Intrc) loctn df   logLik  AICc delta weight
## lm_loc           13.88     + 15 -294.309 622.1  0.00  0.998
## lm_loc_reduced   12.38        2 -315.106 634.3 12.15  0.002
## Models ranked by AICc(x)

model_name	df	logLik	AICc	delta	weight
lm_loc	15	-294.3	622.1	0.00	1
lm_loc_reduced	2	-315.1	634.3	12.15	0

## Analysis of Variance Table
## 
## Model 1: spider_length ~ location
## Model 2: spider_length ~ 1
##   Res.Df    RSS  Df Sum of Sq      F    Pr(>F)    
## 1    138 427.73                                   
## 2    151 562.36 -13   -134.63 3.3412 0.0001843 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

statistics - abdomen length - period2

##  Family: gaussian  ( identity )
## Formula:          abdomen_length ~ urb_cat + (1 | location)
## Data: data_bz_p2
## 
##      AIC      BIC   logLik deviance df.resid 
##    612.8    624.9   -302.4    604.8      148 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance Std.Dev.
##  location (Intercept) 0.486    0.6972  
##  Residual             2.844    1.6864  
## Number of obs: 152, groups:  location, 14
## 
## Dispersion estimate for gaussian family (sigma^2): 2.84 
## 
## Conditional model:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       9.5201     0.3302  28.827   <2e-16 ***
## urb_catHIGHHIGH   0.4003     0.4648   0.861    0.389    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: abdomen_length
##                Chisq Df Pr(>Chisq)    
## (Intercept) 831.0203  1     <2e-16 ***
## urb_cat       0.7417  1     0.3891    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = abdomen_length ~ location, data = data_bz_p2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -3.9842 -0.9458  0.0630  1.0665  5.2538 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    11.0152     0.5623  19.589  < 2e-16 ***
## locationP02SR  -0.7783     0.7207  -1.080  0.28210    
## locationP03SR  -1.8012     0.7952  -2.265  0.02507 *  
## locationP08SG   0.2946     0.7582   0.389  0.69822    
## locationP09SG  -2.0114     0.7315  -2.750  0.00677 ** 
## locationP11SR   0.1550     0.7582   0.204  0.83837    
## locationP12SR  -2.0365     0.7315  -2.784  0.00612 ** 
## locationP17SG  -2.4521     0.8501  -2.884  0.00455 ** 
## locationP18SG  -0.7655     0.7582  -1.010  0.31446    
## locationP19SR  -1.8843     0.7582  -2.485  0.01414 *  
## locationP21SR  -1.2682     0.7751  -1.636  0.10408    
## locationP25SG  -1.7716     0.7582  -2.336  0.02091 *  
## locationP26SG  -1.5452     0.8501  -1.818  0.07129 .  
## locationP27SG  -2.2726     0.7113  -3.195  0.00173 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.687 on 138 degrees of freedom
## Multiple R-squared:  0.2332, Adjusted R-squared:  0.1609 
## F-statistic: 3.228 on 13 and 138 DF,  p-value: 0.0002826

## Anova Table (Type III tests)
## 
## Response: abdomen_length
##              Sum Sq  Df F value    Pr(>F)    
## (Intercept) 1092.02   1 383.727 < 2.2e-16 ***
## location     119.42  13   3.228 0.0002826 ***
## Residuals    392.72 138                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##  contrast      estimate    SE  df t.ratio p.value
##  P01SR - P02SR   0.7783 0.721 138   1.080  0.9983
##  P01SR - P03SR   1.8012 0.795 138   2.265  0.5833
##  P01SR - P08SG  -0.2946 0.758 138  -0.389  1.0000
##  P01SR - P09SG   2.0114 0.732 138   2.750  0.2631
##  P01SR - P11SR  -0.1550 0.758 138  -0.204  1.0000
##  P01SR - P12SR   2.0365 0.732 138   2.784  0.2451
##  P01SR - P17SG   2.4521 0.850 138   2.884  0.1974
##  P01SR - P18SG   0.7655 0.758 138   1.010  0.9991
##  P01SR - P19SR   1.8843 0.758 138   2.485  0.4260
##  P01SR - P21SR   1.2682 0.775 138   1.636  0.9332
##  P01SR - P25SG   1.7716 0.758 138   2.336  0.5314
##  P01SR - P26SG   1.5452 0.850 138   1.818  0.8644
##  P01SR - P27SG   2.2726 0.711 138   3.195  0.0923
##  P02SR - P03SR   1.0229 0.721 138   1.419  0.9781
##  P02SR - P08SG  -1.0729 0.680 138  -1.578  0.9488
##  P02SR - P09SG   1.2331 0.650 138   1.898  0.8242
##  P02SR - P11SR  -0.9333 0.680 138  -1.373  0.9835
##  P02SR - P12SR   1.2582 0.650 138   1.936  0.8028
##  P02SR - P17SG   1.6738 0.781 138   2.143  0.6701
##  P02SR - P18SG  -0.0128 0.680 138  -0.019  1.0000
##  P02SR - P19SR   1.1060 0.680 138   1.627  0.9359
##  P02SR - P21SR   0.4899 0.698 138   0.701  1.0000
##  P02SR - P25SG   0.9933 0.680 138   1.461  0.9721
##  P02SR - P26SG   0.7669 0.781 138   0.982  0.9994
##  P02SR - P27SG   1.4943 0.627 138   2.384  0.4973
##  P03SR - P08SG  -2.0958 0.758 138  -2.764  0.2554
##  P03SR - P09SG   0.2102 0.732 138   0.287  1.0000
##  P03SR - P11SR  -1.9562 0.758 138  -2.580  0.3631
##  P03SR - P12SR   0.2353 0.732 138   0.322  1.0000
##  P03SR - P17SG   0.6509 0.850 138   0.766  1.0000
##  P03SR - P18SG  -1.0357 0.758 138  -1.366  0.9842
##  P03SR - P19SR   0.0831 0.758 138   0.110  1.0000
##  P03SR - P21SR  -0.5330 0.775 138  -0.688  1.0000
##  P03SR - P25SG  -0.0296 0.758 138  -0.039  1.0000
##  P03SR - P26SG  -0.2560 0.850 138  -0.301  1.0000
##  P03SR - P27SG   0.4714 0.711 138   0.663  1.0000
##  P08SG - P09SG   2.3060 0.691 138   3.337  0.0626
##  P08SG - P11SR   0.1396 0.719 138   0.194  1.0000
##  P08SG - P12SR   2.3311 0.691 138   3.373  0.0564
##  P08SG - P17SG   2.7467 0.816 138   3.368  0.0573
##  P08SG - P18SG   1.0601 0.719 138   1.474  0.9701
##  P08SG - P19SR   2.1789 0.719 138   3.029  0.1408
##  P08SG - P21SR   1.5628 0.737 138   2.120  0.6861
##  P08SG - P25SG   2.0662 0.719 138   2.872  0.2027
##  P08SG - P26SG   1.8398 0.816 138   2.256  0.5900
##  P08SG - P27SG   2.5672 0.670 138   3.834  0.0133
##  P09SG - P11SR  -2.1663 0.691 138  -3.135  0.1081
##  P09SG - P12SR   0.0252 0.662 138   0.038  1.0000
##  P09SG - P17SG   0.4407 0.791 138   0.557  1.0000
##  P09SG - P18SG  -1.2459 0.691 138  -1.803  0.8712
##  P09SG - P19SR  -0.1271 0.691 138  -0.184  1.0000
##  P09SG - P21SR  -0.7432 0.710 138  -1.047  0.9988
##  P09SG - P25SG  -0.2398 0.691 138  -0.347  1.0000
##  P09SG - P26SG  -0.4662 0.791 138  -0.589  1.0000
##  P09SG - P27SG   0.2612 0.639 138   0.409  1.0000
##  P11SR - P12SR   2.1915 0.691 138   3.171  0.0983
##  P11SR - P17SG   2.6070 0.816 138   3.196  0.0920
##  P11SR - P18SG   0.9205 0.719 138   1.280  0.9912
##  P11SR - P19SR   2.0393 0.719 138   2.835  0.2200
##  P11SR - P21SR   1.4232 0.737 138   1.931  0.8060
##  P11SR - P25SG   1.9265 0.719 138   2.678  0.3029
##  P11SR - P26SG   1.7002 0.816 138   2.084  0.7104
##  P11SR - P27SG   2.4276 0.670 138   3.625  0.0263
##  P12SR - P17SG   0.4155 0.791 138   0.525  1.0000
##  P12SR - P18SG  -1.2710 0.691 138  -1.839  0.8542
##  P12SR - P19SR  -0.1522 0.691 138  -0.220  1.0000
##  P12SR - P21SR  -0.7683 0.710 138  -1.083  0.9982
##  P12SR - P25SG  -0.2649 0.691 138  -0.383  1.0000
##  P12SR - P26SG  -0.4913 0.791 138  -0.621  1.0000
##  P12SR - P27SG   0.2361 0.639 138   0.369  1.0000
##  P17SG - P18SG  -1.6866 0.816 138  -2.068  0.7215
##  P17SG - P19SR  -0.5678 0.816 138  -0.696  1.0000
##  P17SG - P21SR  -1.1839 0.831 138  -1.424  0.9775
##  P17SG - P25SG  -0.6805 0.816 138  -0.834  0.9999
##  P17SG - P26SG  -0.9069 0.902 138  -1.006  0.9992
##  P17SG - P27SG  -0.1795 0.772 138  -0.232  1.0000
##  P18SG - P19SR   1.1188 0.719 138   1.555  0.9542
##  P18SG - P21SR   0.5027 0.737 138   0.682  1.0000
##  P18SG - P25SG   1.0061 0.719 138   1.399  0.9806
##  P18SG - P26SG   0.7797 0.816 138   0.956  0.9995
##  P18SG - P27SG   1.5071 0.670 138   2.251  0.5937
##  P19SR - P21SR  -0.6161 0.737 138  -0.836  0.9999
##  P19SR - P25SG  -0.1127 0.719 138  -0.157  1.0000
##  P19SR - P26SG  -0.3391 0.816 138  -0.416  1.0000
##  P19SR - P27SG   0.3883 0.670 138   0.580  1.0000
##  P21SR - P25SG   0.5034 0.737 138   0.683  1.0000
##  P21SR - P26SG   0.2770 0.831 138   0.333  1.0000
##  P21SR - P27SG   1.0044 0.689 138   1.458  0.9725
##  P25SG - P26SG  -0.2264 0.816 138  -0.278  1.0000
##  P25SG - P27SG   0.5010 0.670 138   0.748  1.0000
##  P26SG - P27SG   0.7274 0.772 138   0.942  0.9996
## 
## P value adjustment: tukey method for comparing a family of 14 estimates

contrast	estimate	SE	df	t.ratio	p.value
P01SR - P27SG	2.272622	0.7112828	138	3.195104	0.0922645
P08SG - P09SG	2.305972	0.6911005	138	3.336667	0.0625562
P08SG - P12SR	2.331126	0.6911005	138	3.373064	0.0563837
P08SG - P17SG	2.746675	0.8156324	138	3.367541	0.0572855
P08SG - P27SG	2.567218	0.6696506	138	3.833668	0.0133144
P11SR - P12SR	2.191489	0.6911005	138	3.171014	0.0983237
P11SR - P17SG	2.607039	0.8156324	138	3.196341	0.0919617
P11SR - P27SG	2.427582	0.6696506	138	3.625147	0.0263282

result: same as for spider length

in summary: first result for spider length then for abdomen length in bold the differences between the two tables | contrast | estimate | SE | df | t.ratio | p.value | |:——————|———-:|———-:|—-:|———-:|———-:| | P01SR - P17SG | 2.889778 | 0.8872288 | 138 | 3.257083 | 0.0780696 | | P01SR - P27SG | 2.726444 | 0.7423088 | 138 | 3.672925 | 0.0226102 | | P08SG - P09SG | 2.370238 | 0.7212461 | 138 | 3.286309 | 0.0720350 | | P08SG - P17SG | 2.894545 | 0.8512101 | 138 | 3.400506 | 0.0520815 | | P08SG - P27SG | 2.731212 | 0.6988607 | 138 | 3.908092 | 0.0103288 | | P09SG - P11SR | -2.321238 | 0.7212461 | 138 | -3.218371 | 0.0867053 | | P11SR - P17SG | 2.845546 | 0.8512101 | 138 | 3.342941 | 0.0614529 | | P11SR - P27SG | 2.682212 | 0.6988607 | 138 | 3.837978 | 0.0131220 |

contrast	estimate	SE	df	t.ratio	p.value
P01SR - P27SG	2.272622	0.7112828	138	3.195104	0.0922645
P08SG - P09SG	2.305972	0.6911005	138	3.336667	0.0625562
P08SG - P12SR	2.331126	0.6911005	138	3.373064	0.0563837
P08SG - P17SG	2.746675	0.8156324	138	3.367541	0.0572855
P08SG - P27SG	2.567218	0.6696506	138	3.833668	0.0133144
P11SR - P12SR	2.191489	0.6911005	138	3.171014	0.0983237
P11SR - P17SG	2.607039	0.8156324	138	3.196341	0.0919617
P11SR - P27SG	2.427582	0.6696506	138	3.625147	0.0263282

statistics - abdomen area

abdomen area as a potential measure of fecundity

##  Family: gaussian  ( identity )
## Formula:          abdomen_area ~ urb_cat + (1 | location)
## Data: data_bz_p2
## 
##      AIC      BIC   logLik deviance df.resid 
##   1402.2   1414.3   -697.1   1394.2      148 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance Std.Dev.
##  location (Intercept)  96.06    9.801  
##  Residual             509.28   22.567  
## Number of obs: 152, groups:  location, 14
## 
## Dispersion estimate for gaussian family (sigma^2):  509 
## 
## Conditional model:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       60.920      4.564  13.348   <2e-16 ***
## urb_catHIGHHIGH    4.861      6.426   0.756    0.449    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: abdomen_area
##                Chisq Df Pr(>Chisq)    
## (Intercept) 178.1595  1     <2e-16 ***
## urb_cat       0.5722  1     0.4494    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## 
## Call:
## lm(formula = abdomen_area ~ location, data = data_bz_p2)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53.03 -12.91  -0.44  12.37  88.58 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept)    83.7657     7.5231  11.134  < 2e-16 ***
## locationP02SR -15.8807     9.6427  -1.647 0.101851    
## locationP03SR -28.4176    10.6393  -2.671 0.008472 ** 
## locationP08SG   1.5885    10.1442   0.157 0.875794    
## locationP09SG -29.9503     9.7868  -3.060 0.002658 ** 
## locationP11SR  -0.8391    10.1442  -0.083 0.934195    
## locationP12SR -31.5641     9.7868  -3.225 0.001572 ** 
## locationP17SG -35.5747    11.3739  -3.128 0.002149 ** 
## locationP18SG -14.5448    10.1442  -1.434 0.153891    
## locationP19SR -28.8829    10.1442  -2.847 0.005084 ** 
## locationP21SR -19.2827    10.3699  -1.859 0.065089 .  
## locationP25SG -26.2320    10.1442  -2.586 0.010748 *  
## locationP26SG -23.3042    11.3739  -2.049 0.042365 *  
## locationP27SG -32.6605     9.5161  -3.432 0.000791 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 22.57 on 138 degrees of freedom
## Multiple R-squared:  0.2418, Adjusted R-squared:  0.1704 
## F-statistic: 3.385 on 13 and 138 DF,  p-value: 0.0001559

## Anova Table (Type III tests)
## 
## Response: abdomen_area
##             Sum Sq  Df  F value    Pr(>F)    
## (Intercept)  63150   1 123.9748 < 2.2e-16 ***
## location     22418  13   3.3855 0.0001559 ***
## Residuals    70294 138                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

##  contrast      estimate    SE  df t.ratio p.value
##  P01SR - P02SR   15.881  9.64 138   1.647  0.9300
##  P01SR - P03SR   28.418 10.64 138   2.671  0.3071
##  P01SR - P08SG   -1.589 10.14 138  -0.157  1.0000
##  P01SR - P09SG   29.950  9.79 138   3.060  0.1304
##  P01SR - P11SR    0.839 10.14 138   0.083  1.0000
##  P01SR - P12SR   31.564  9.79 138   3.225  0.0851
##  P01SR - P17SG   35.575 11.37 138   3.128  0.1100
##  P01SR - P18SG   14.545 10.14 138   1.434  0.9761
##  P01SR - P19SR   28.883 10.14 138   2.847  0.2142
##  P01SR - P21SR   19.283 10.37 138   1.859  0.8441
##  P01SR - P25SG   26.232 10.14 138   2.586  0.3592
##  P01SR - P26SG   23.304 11.37 138   2.049  0.7339
##  P01SR - P27SG   32.660  9.52 138   3.432  0.0475
##  P02SR - P03SR   12.537  9.64 138   1.300  0.9898
##  P02SR - P08SG  -17.469  9.09 138  -1.921  0.8114
##  P02SR - P09SG   14.070  8.69 138   1.619  0.9383
##  P02SR - P11SR  -15.042  9.09 138  -1.654  0.9278
##  P02SR - P12SR   15.683  8.69 138   1.804  0.8706
##  P02SR - P17SG   19.694 10.45 138   1.885  0.8310
##  P02SR - P18SG   -1.336  9.09 138  -0.147  1.0000
##  P02SR - P19SR   13.002  9.09 138   1.430  0.9767
##  P02SR - P21SR    3.402  9.34 138   0.364  1.0000
##  P02SR - P25SG   10.351  9.09 138   1.138  0.9971
##  P02SR - P26SG    7.423 10.45 138   0.711  1.0000
##  P02SR - P27SG   16.780  8.39 138   2.001  0.7646
##  P03SR - P08SG  -30.006 10.14 138  -2.958  0.1669
##  P03SR - P09SG    1.533  9.79 138   0.157  1.0000
##  P03SR - P11SR  -27.578 10.14 138  -2.719  0.2800
##  P03SR - P12SR    3.147  9.79 138   0.322  1.0000
##  P03SR - P17SG    7.157 11.37 138   0.629  1.0000
##  P03SR - P18SG  -13.873 10.14 138  -1.368  0.9841
##  P03SR - P19SR    0.465 10.14 138   0.046  1.0000
##  P03SR - P21SR   -9.135 10.37 138  -0.881  0.9998
##  P03SR - P25SG   -2.186 10.14 138  -0.215  1.0000
##  P03SR - P26SG   -5.113 11.37 138  -0.450  1.0000
##  P03SR - P27SG    4.243  9.52 138   0.446  1.0000
##  P08SG - P09SG   31.539  9.25 138   3.411  0.0505
##  P08SG - P11SR    2.428  9.62 138   0.252  1.0000
##  P08SG - P12SR   33.153  9.25 138   3.586  0.0298
##  P08SG - P17SG   37.163 10.91 138   3.406  0.0513
##  P08SG - P18SG   16.133  9.62 138   1.676  0.9207
##  P08SG - P19SR   30.471  9.62 138   3.166  0.0995
##  P08SG - P21SR   20.871  9.86 138   2.116  0.6888
##  P08SG - P25SG   27.821  9.62 138   2.891  0.1946
##  P08SG - P26SG   24.893 10.91 138   2.281  0.5715
##  P08SG - P27SG   34.249  8.96 138   3.823  0.0138
##  P09SG - P11SR  -29.111  9.25 138  -3.148  0.1043
##  P09SG - P12SR    1.614  8.85 138   0.182  1.0000
##  P09SG - P17SG    5.624 10.58 138   0.532  1.0000
##  P09SG - P18SG  -15.406  9.25 138  -1.666  0.9240
##  P09SG - P19SR   -1.067  9.25 138  -0.115  1.0000
##  P09SG - P21SR  -10.668  9.49 138  -1.124  0.9975
##  P09SG - P25SG   -3.718  9.25 138  -0.402  1.0000
##  P09SG - P26SG   -6.646 10.58 138  -0.628  1.0000
##  P09SG - P27SG    2.710  8.55 138   0.317  1.0000
##  P11SR - P12SR   30.725  9.25 138   3.323  0.0650
##  P11SR - P17SG   34.736 10.91 138   3.183  0.0952
##  P11SR - P18SG   13.706  9.62 138   1.424  0.9775
##  P11SR - P19SR   28.044  9.62 138   2.914  0.1846
##  P11SR - P21SR   18.444  9.86 138   1.870  0.8386
##  P11SR - P25SG   25.393  9.62 138   2.639  0.3265
##  P11SR - P26SG   22.465 10.91 138   2.059  0.7275
##  P11SR - P27SG   31.821  8.96 138   3.552  0.0331
##  P12SR - P17SG    4.011 10.58 138   0.379  1.0000
##  P12SR - P18SG  -17.019  9.25 138  -1.841  0.8534
##  P12SR - P19SR   -2.681  9.25 138  -0.290  1.0000
##  P12SR - P21SR  -12.281  9.49 138  -1.294  0.9903
##  P12SR - P25SG   -5.332  9.25 138  -0.577  1.0000
##  P12SR - P26SG   -8.260 10.58 138  -0.781  0.9999
##  P12SR - P27SG    1.096  8.55 138   0.128  1.0000
##  P17SG - P18SG  -21.030 10.91 138  -1.927  0.8080
##  P17SG - P19SR   -6.692 10.91 138  -0.613  1.0000
##  P17SG - P21SR  -16.292 11.12 138  -1.465  0.9715
##  P17SG - P25SG   -9.343 10.91 138  -0.856  0.9999
##  P17SG - P26SG  -12.270 12.06 138  -1.017  0.9991
##  P17SG - P27SG   -2.914 10.33 138  -0.282  1.0000
##  P18SG - P19SR   14.338  9.62 138   1.490  0.9674
##  P18SG - P21SR    4.738  9.86 138   0.480  1.0000
##  P18SG - P25SG   11.687  9.62 138   1.214  0.9946
##  P18SG - P26SG    8.759 10.91 138   0.803  0.9999
##  P18SG - P27SG   18.116  8.96 138   2.022  0.7512
##  P19SR - P21SR   -9.600  9.86 138  -0.974  0.9994
##  P19SR - P25SG   -2.651  9.62 138  -0.275  1.0000
##  P19SR - P26SG   -5.579 10.91 138  -0.511  1.0000
##  P19SR - P27SG    3.778  8.96 138   0.422  1.0000
##  P21SR - P25SG    6.949  9.86 138   0.705  1.0000
##  P21SR - P26SG    4.022 11.12 138   0.362  1.0000
##  P21SR - P27SG   13.378  9.21 138   1.452  0.9735
##  P25SG - P26SG   -2.928 10.91 138  -0.268  1.0000
##  P25SG - P27SG    6.428  8.96 138   0.718  1.0000
##  P26SG - P27SG    9.356 10.33 138   0.906  0.9997
## 
## P value adjustment: tukey method for comparing a family of 14 estimates

contrast	estimate	SE	df	t.ratio	p.value
P01SR - P12SR	31.56405	9.786760	138	3.225179	0.0851320
P01SR - P27SG	32.66047	9.516106	138	3.432125	0.0474777
P08SG - P09SG	31.53880	9.246090	138	3.411042	0.0505065
P08SG - P12SR	33.15257	9.246090	138	3.585577	0.0298110
P08SG - P17SG	37.16318	10.912177	138	3.405661	0.0513056
P08SG - P27SG	34.24898	8.959117	138	3.822808	0.0138107
P11SR - P12SR	30.72493	9.246090	138	3.323019	0.0650144
P11SR - P17SG	34.73555	10.912177	138	3.183191	0.0952216
P11SR - P27SG	31.82135	8.959117	138	3.551839	0.0330974

colour -

all photos were redone by me with the macro and R-script of Maxime to correct the RGB values % reflectance => variable : avg_reflectance in dataset SC22_data

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   11.68   16.00   17.99   18.49   20.18   32.21

also here large differences between the first and the second sampling period

correlation colour: there is a negative correlation between avg_reflectance and sampling day and there is ofcourse a positive relation between sampling period and day

explorative graphs for the full data :

if you verify the correlation % reflectance and sampling day for the different sampling set-ups periods: so for period1 dataset and period2 dataset

visualisation of the sampling periods Immediatly shows the differences in the datasets again

the same 3 graph visualisations per sampling period dataset for reflectance period1 :

period2: (remark P16SG only 1 indiv)

##statistics ### full dataset SC22_data i think it is difficult to combine the data of sampling period1 and period2

i don’t think these first models: mod1, mod2 are correct. because sampling day is clearly correlated with sampling periods. and generally also spiders are darker later in the sampling season

##  Family: gaussian  ( identity )
## Formula:          
## avg_reflectance ~ U_landscape + U_local + sday + (1 | plotid/location)
## Data: SC22_data
## 
##      AIC      BIC   logLik deviance df.resid 
##   3221.8   3257.3  -1602.9   3205.8      617 
## 
## Random effects:
## 
## Conditional model:
##  Groups          Name        Variance Std.Dev.
##  location:plotid (Intercept) 0.4793   0.6923  
##  plotid          (Intercept) 0.7913   0.8896  
##  Residual                    9.1891   3.0313  
## Number of obs: 625, groups:  location:plotid, 54; plotid, 27
## 
## Dispersion estimate for gaussian family (sigma^2): 9.19 
## 
## Conditional model:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        18.2832     0.4197   43.56   <2e-16 ***
## U_landscapeMEDIUM   0.2147     0.5821    0.37    0.712    
## U_landscapeHIGH     0.1517     0.5644    0.27    0.788    
## U_localHIGH         0.1582     0.3196    0.49    0.621    
## sday               -1.3743     0.1593   -8.63   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: avg_reflectance
##                 Chisq Df Pr(>Chisq)    
## (Intercept) 1897.2746  1     <2e-16 ***
## U_landscape    0.1462  2     0.9295    
## U_local        0.2450  1     0.6206    
## sday          74.4194  1     <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

##  Family: gaussian  ( identity )
## Formula:          
## avg_reflectance ~ U_landscape * U_local + (1 | plotid/location)
## Data: SC22_data
## 
##      AIC      BIC   logLik deviance df.resid 
##   3287.8   3327.7  -1634.9   3269.8      616 
## 
## Random effects:
## 
## Conditional model:
##  Groups          Name        Variance Std.Dev.
##  location:plotid (Intercept)  0.2291  0.4787  
##  plotid          (Intercept)  2.3492  1.5327  
##  Residual                    10.0101  3.1639  
## Number of obs: 625, groups:  location:plotid, 54; plotid, 27
## 
## Dispersion estimate for gaussian family (sigma^2):   10 
## 
## Conditional model:
##                               Estimate Std. Error z value Pr(>|z|)    
## (Intercept)                    17.9302     0.5994  29.912   <2e-16 ***
## U_landscapeMEDIUM               1.0495     0.8819   1.190   0.2340    
## U_landscapeHIGH                 1.0241     0.8795   1.164   0.2442    
## U_localHIGH                     0.9287     0.5049   1.839   0.0659 .  
## U_landscapeMEDIUM:U_localHIGH  -0.7751     0.7548  -1.027   0.3044    
## U_landscapeHIGH:U_localHIGH    -1.6654     0.7066  -2.357   0.0184 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: avg_reflectance
##                        Chisq Df Pr(>Chisq)    
## (Intercept)         894.7290  1    < 2e-16 ***
## U_landscape           1.8948  2    0.38774    
## U_local               3.3830  1    0.06587 .  
## U_landscape:U_local   5.5685  2    0.06177 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

analysis of full dataset : try with urb_cat instead of combination of U_local and U_landscape

##  Family: gaussian  ( identity )
## Formula:          avg_reflectance ~ urb_cat + sday + (1 | location)
## Data: SC22_data
## 
##      AIC      BIC   logLik deviance df.resid 
##   3226.7   3266.7  -1604.4   3208.7      616 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance Std.Dev.
##  location (Intercept) 1.168    1.081   
##  Residual             9.215    3.036   
## Number of obs: 625, groups:  location, 54
## 
## Dispersion estimate for gaussian family (sigma^2): 9.22 
## 
## Conditional model:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)       18.43839    0.44809   41.15   <2e-16 ***
## urb_catLOWHIGH    -0.23566    0.68177   -0.35    0.730    
## urb_catMEDIUMLOW   0.02765    0.67934    0.04    0.968    
## urb_catMEDIUMHIGH  0.18245    0.67973    0.27    0.788    
## urb_catHIGHLOW    -0.24127    0.68180   -0.35    0.723    
## urb_catHIGHHIGH    0.30492    0.62199    0.49    0.624    
## sday              -1.48317    0.15866   -9.35   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: avg_reflectance
##                 Chisq Df Pr(>Chisq)    
## (Intercept) 1693.2514  1     <2e-16 ***
## urb_cat        1.0364  5     0.9596    
## sday          87.3853  1     <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

result: spiders are darker towards the end of the the sampling period result: no difference between urbanisation categories

next i will focus on (i think) correcter analysis, namely the sampling periods separately

sampling period 1:

col_data_p1 contains all 27 plotid, 3 levels of U_landscape and 2 levels of U_local (no intermediate local scale sampling done in 2022)

##  Family: gaussian  ( identity )
## Formula:          
## avg_reflectance ~ U_landscape + U_local + sday + (1 | plotid/location)
## Data: col_data_p1
## 
##      AIC      BIC   logLik deviance df.resid 
##   2453.3   2486.5  -1218.6   2437.3      464 
## 
## Random effects:
## 
## Conditional model:
##  Groups          Name        Variance Std.Dev.
##  location:plotid (Intercept) 0.7772   0.8816  
##  plotid          (Intercept) 0.7743   0.8800  
##  Residual                    9.2863   3.0473  
## Number of obs: 472, groups:  location:plotid, 54; plotid, 27
## 
## Dispersion estimate for gaussian family (sigma^2): 9.29 
## 
## Conditional model:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)        18.6113     0.4738   39.28  < 2e-16 ***
## U_landscapeMEDIUM   0.4162     0.6173    0.67    0.500    
## U_landscapeHIGH     0.1664     0.6147    0.27    0.787    
## U_localHIGH         0.1939     0.3709    0.52    0.601    
## sday               -1.5838     0.2528   -6.26 3.73e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: avg_reflectance
##                 Chisq Df Pr(>Chisq)    
## (Intercept) 1542.8455  1  < 2.2e-16 ***
## U_landscape    0.4604  2     0.7944    
## U_local        0.2732  1     0.6012    
## sday          39.2468  1  3.735e-10 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

sampling period 2

contains 7 high-high and 8 urbanisation category locations
highhigh : P01SR P02SR P03SR P11SR P12SR P19SR P21SR lowlow : P08SG P09SG P16SG P17SG P18SG P25SG P26SG P27SG

remove P16SG from the dataset as it only contains 1 spider (also done for body size measurements) so then 7 high high locations and 7 low low locations

##  Family: gaussian  ( identity )
## Formula:          avg_reflectance ~ urb_cat + sday + (1 | location)
## Data: col_data_p2
## 
##      AIC      BIC   logLik deviance df.resid 
##    756.4    771.5   -373.2    746.4      147 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance Std.Dev.
##  location (Intercept) 0.3732   0.6109  
##  Residual             7.6429   2.7646  
## Number of obs: 152, groups:  location, 14
## 
## Dispersion estimate for gaussian family (sigma^2): 7.64 
## 
## Conditional model:
##                 Estimate Std. Error z value Pr(>|z|)    
## (Intercept)      17.3119     0.3976   43.54  < 2e-16 ***
## urb_catHIGHHIGH  -0.1419     0.5611   -0.25  0.80035    
## sday             -0.7697     0.2757   -2.79  0.00524 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: avg_reflectance
##                 Chisq Df Pr(>Chisq)    
## (Intercept) 1895.4475  1  < 2.2e-16 ***
## urb_cat        0.0640  1   0.800349    
## sday           7.7952  1   0.005239 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector
## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

result: only sampling day is significant / no differences between the urbanisation catagories

P01SR was relatively early resampled (are the samples from the Coupure in Ghent COUP01…) I repeat the same analysis for sampling period 2 without P01SR to see if there are differences in the results

##  Family: gaussian  ( identity )
## Formula:          avg_reflectance ~ urb_cat * sday + (1 | location)
## Data: col_data_p2_sub
## 
##      AIC      BIC   logLik deviance df.resid 
##    703.4    721.2   -345.7    691.4      137 
## 
## Random effects:
## 
## Conditional model:
##  Groups   Name        Variance  Std.Dev.
##  location (Intercept) 2.079e-06 0.001442
##  Residual             7.370e+00 2.714723
## Number of obs: 143, groups:  location, 13
## 
## Dispersion estimate for gaussian family (sigma^2): 7.37 
## 
## Conditional model:
##                      Estimate Std. Error z value Pr(>|z|)    
## (Intercept)           17.2252     0.3204   53.76   <2e-16 ***
## urb_catHIGHHIGH        0.0987     0.4650    0.21   0.8319    
## sday                  -0.2579     0.3318   -0.78   0.4370    
## urb_catHIGHHIGH:sday  -1.1869     0.4661   -2.55   0.0109 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: avg_reflectance
##                  Chisq Df Pr(>Chisq)    
## (Intercept)  2890.6097  1    < 2e-16 ***
## urb_cat         0.0451  1    0.83190    
## sday            0.6041  1    0.43701    
## urb_cat:sday    6.4845  1    0.01088 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

## Warning in Analyze.model(focal.predictors, mod, xlevels, default.levels, : the
## predictor sday is a one-column matrix that was converted to a vector

##  contrast                                                                 
##  (LOWLOW sday-3.68888928211885e-15) - (HIGHHIGH sday-3.68888928211885e-15)
##  estimate    SE  df t.ratio p.value
##   -0.0987 0.465 137  -0.212  0.8322

result : if P01SR removed from the second sampling period. and then verified if there are effects of urbanisation categorie (lowlow vs highhigh) and/or effects of sampling date: then we get the result that urb_cat::sday is significant